15‑Step AI Roadmap

A 15-step post from Andrew Bolis is circulating as a practical map for learning artificial intelligence, starting with chatbots and ending with agentic systems. (x.com) Bolis’s post lays out a progression from basic prompting and chatbot use to workflows, agents, and “agentic AI,” the label many creators now use for systems that plan tasks and call tools with limited autonomy. Bolis’s account site identifies him as an AI and marketing consultant and former chief marketing officer. (x.com) (andrewbolis.com) The post had about 75 likes and roughly 5,700 views when it was cited in the source material, a modest count by consumer-internet standards but enough to show pickup inside the online AI learning niche. X did not return readable page text through the browser tool, so those engagement figures come from the provided post reference rather than a machine-readable page scrape. (x.com) The roadmap format has become common because AI learning has split into layers. A chatbot is the front-end conversation tool, retrieval-augmented generation adds outside documents, and an agent goes further by choosing steps and using software tools to finish a job. (microsoft.github.io) (github.com) That shift has produced a market for “what to learn next” guides. Public GitHub roadmaps now package free courses, repos, papers, and videos into step-by-step tracks for beginners and working developers. (github.com 1) (github.com 2) (github.com 3) Other creators named in the discussion are pushing the same bundle-and-curate model. The referenced X posts from Manisha Mishra and Igor Buinevici were cited as companion lists of repos, videos, and free courses, though X again did not expose readable text through the browser tool for direct extraction. (x.com 1) (x.com 2) Bolis has posted similar education threads before. Separate write-ups indexed on the web describe his late-2025 posts on AI agents, agentic AI, prompt engineering, Stanford’s free AI courses, and free courses for building agents, suggesting he has been turning short social posts into serialized learning guides for months. (enterprisezone.cc 1) (enterprisezone.cc 2) (enterprisezone.cc 3) (enterprisezone.cc 4) (enterprisezone.cc 5) The appeal is straightforward: many learners do not need a research degree to start using AI, but they do need an order of operations. These roadmaps usually start with prompting and model basics, then move to Python, application programming interfaces, vector databases, orchestration frameworks, and evaluation. (github.com) (github.com) (github.com) They also flatten very different goals into one ladder. Someone trying to automate marketing tasks, someone building production software, and someone studying machine learning theory may all see “AI roadmap” and mean different things. (andrewbolis.com) (github.com) (github.com) What the Bolis post captures is the current center of gravity in AI education: less focus on using one chatbot well, more focus on chaining models, tools, memory, and workflows into systems that can do work. The thread is short, but it fits a much larger scramble to turn AI from a curiosity into a repeatable skill stack. (x.com) (github.com)